- Title
- Robust piecewise linear L 1-regression via nonsmooth DC optimization
- Creator
- Bagirov, Adil; Taheri, Sona; Karmitsa, Napsu; Sultanova, Nargiz; Asadi, Soodabeh
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/191855
- Identifier
- vital:17890
- Identifier
-
https://doi.org/10.1080/10556788.2020.1855171
- Identifier
- ISSN:1055-6788 (ISSN)
- Abstract
- Piecewise linear (Formula presented.) -regression problem is formulated as an unconstrained difference of convex (DC) optimization problem and an algorithm for solving this problem is developed. Auxiliary problems are introduced to design an adaptive approach to generate a suitable piecewise linear regression model and starting points for solving the underlying DC optimization problems. The performance of the proposed algorithm as both approximation and prediction tool is evaluated using synthetic and real-world data sets containing outliers. It is also compared with mainstream machine learning regression algorithms using various performance measures. Results demonstrate that the new algorithm is robust to outliers and in general, provides better predictions than the other alternative regression algorithms for most data sets used in the numerical experiments. © 2020 Informa UK Limited, trading as Taylor & Francis Group.
- Publisher
- Taylor and Francis Ltd.
- Relation
- Optimization Methods and Software Vol. 37, no. 4 (2022), p. 1289-1309; http://purl.org/au-research/grants/arc/DP190100580
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2020 Informa UK Limited
- Subject
- 4602 Artificial intelligence; 4901 Applied mathematics; 4903 Numerical and computational mathematics; 65K05; 90C25; DC optimization; Least Absolute Deviation Regression; Nonconvex Optimization; Nonsmooth Optimization; Outliers; Regression Analysis
- Reviewed
- Funder
- The research by Dr. Adil Bagirov and Dr. Sona Taheri was supported by the Australian Government through the Australian Research Council's Discovery Projects funding scheme (Project No. DP190100580). The research by Dr. Napsu Karmitsa was supported by the Academy of Finland (Project No. 289500 and 319274).
- Hits: 3121
- Visitors: 3008
- Downloads: 0
| Thumbnail | File | Description | Size | Format |
|---|